Learning-Based Resource Management in Integrated Sensing and Communication Systems
This work addresses resource management for integrated sensing and communication systems, which is an incremental improvement in a domain-specific area.
The paper tackles adaptive time allocation in integrated sensing and communication systems to optimize resource management between tracking multiple targets and data transmission, resulting in enhanced communication quality under time constraints as demonstrated by numerical results.
In this paper, we tackle the task of adaptive time allocation in integrated sensing and communication systems equipped with radar and communication units. The dual-functional radar-communication system's task involves allocating dwell times for tracking multiple targets and utilizing the remaining time for data transmission towards estimated target locations. We introduce a novel constrained deep reinforcement learning (CDRL) approach, designed to optimize resource allocation between tracking and communication under time budget constraints, thereby enhancing target communication quality. Our numerical results demonstrate the efficiency of our proposed CDRL framework, confirming its ability to maximize communication quality in highly dynamic environments while adhering to time constraints.